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A Classification Model on Graduate Employability Using Bayesian Approaches: A Comparison

Bangsuk Jantawan,, Cheng-Fa Tsai

The aim of study presents a graduate employability model that uses Bayesian methods to search the most important factor of graduate employability, and to compare the accuracy of each algorithm under Bayesian methods including Naïve Bayesian Simple, Naïve Bayesian, Averaged One-Dependence Estimators, Averaged One- Dependence Estimators with subsumption resolution, Bayesian networks, and Naïve Bayesian Updateable. The results show that 3 factors with a direct effect on employability are the work province, occupation type, and times find work.